Learning classification rules based on concept semilattice

Concept lattice is an efficient formal tool for data analysis and knowledge extraction. In this paper, we present an incremental construction algorithm of join-semilattice with a simple example and a novel induction algorithm, rulextracter, which induces classification rules using a semilattice as a...

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Hauptverfasser: Chengming Qi, Shoumei Cui, Yunchuan Sun
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Yunchuan Sun
description Concept lattice is an efficient formal tool for data analysis and knowledge extraction. In this paper, we present an incremental construction algorithm of join-semilattice with a simple example and a novel induction algorithm, rulextracter, which induces classification rules using a semilattice as an explicit map through the search space of rules. Furthermore, our learning system is shown to be robust in the presence of noisy data. The rulextracter system is also capable of learning both decision lists as well as unordered rule sets and thus allows for comparisons of these different learning paradigms within the same algorithmic framework.
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subjects Communication system control
Concept semilattice
Data analysis
Data mining
Electronic mail
Formal concept analysis (FCA)
Incremental formation
Knowledge acquisition
Lattices
Learning systems
Robustness
Rules extraction
Software engineering
Sun
title Learning classification rules based on concept semilattice
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